Summary Training Language Models With Pause Tokens arxiv.org
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The research paper proposes using pause tokens during training as a method to enhance language model performance, referred to as "pause-training".
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Key Points
- "Pause-training" is a new approach to training language models that involves appending pause tokens to the input prefix and delaying the model's outputs until the last pause token is seen.
- Pause-training showed gains on various downstream tasks, including an 18% improvement in exact match score on the SQuAD question-answering task compared to the standard model.
- Introducing delays in both pretraining and finetuning yielded the best results, while introducing delays only during finetuning showed mixed results.
- Appending pause tokens was generally better than prepending them, and there was an optimal number of pause tokens for each downstream task.
- Pause-trained models were relatively robust to test-time distribution shifts but performed significantly worse when provided with zero delay during inference.
Summaries
16 word summary
The research paper suggests "pause-training" to improve language model performance by using pause tokens during training.
59 word summary
The research paper proposes "pause-training" to improve language model performance. Pause tokens are used during training to allow the model to utilize additional computation during inference. Training with pause tokens enhances performance on various tasks. The paper explains the Transformer model used in experiments and shows that pause training outperforms standard training on reasoning, fact recall, and question-answering tasks.
164 word summary
The research paper proposes a new approach called "pause-training" to improve language model performance. It suggests using "pause" tokens during training to allow the model to utilize additional computation during inference. This approach differs from the traditional next-token prediction paradigm. Training with pause tokens enhances performance on various tasks, but not all tasks may benefit from this approach. The cost of pause pretraining limits widespread use. Future research should explore gains across different model sizes and architectures, understand the mechanism of pause tokens, and investigate different algorithms for pause training. The paper provides a detailed explanation of the Transformer model used in experiments and presents results showing that pause training outperforms standard training on reasoning, fact recall, and question-answering tasks. Varying the number and placement of pause tokens affects performance, and pause training is robust to shifts in the number of inference-time pauses. Overall, the paper introduces pause tokens for training language models and opens up new avenues for research in delayed next-token prediction.
398 word summary
The research paper "Training Language Models With Pause Tokens" proposes a new approach called "pause-training" to improve the performance of language models. The authors suggest using "pause" tokens during training to allow the model to utilize additional computation during inference. This approach deviates from the traditional immediate next-token prediction paradigm.
The authors demonstrate that training with pause tokens can enhance performance on various tasks. However, they note that not all tasks may benefit from this approach, and some tasks may be better suited without pause tokens. Additionally, the authors mention that the cost of pause pretraining makes it less accessible for widespread use. They also highlight several areas for future research, including exploring gains across different model sizes and architectures, understanding the mechanism of pause tokens, and investigating different algorithms for pause training.
The paper provides a detailed explanation of the Transformer model used in their experiments. It describes the operations involved in the Transformer block and the generation of the next token. The authors present additional results on downstream finetuning performance for both a 1B model and a 130M model. These results show that pause training outperforms standard training on various tasks, including reasoning tasks, fact recall tasks, and question-answering tasks.
The authors also investigate the impact of prepending or appending pause tokens during training. They find that appending the pause tokens generally leads to better performance, although there are some mixed results depending on the task. They further explore the effect of varying the number of pause tokens used during finetuning and discover that there is an optimal number of pause tokens for each dataset.
The paper examines the robustness of pause-trained models to shifts in the number of inference-time pauses compared to the number used during finetuning. It is observed that pause training gracefully degrades with shifts in the number of tokens seen, except for one task where there is a drop in performance when the delay is removed during inference.
In conclusion, the paper introduces the concept of pause tokens for training language models and demonstrates their effectiveness on various tasks. The authors provide detailed explanations of the Transformer model, experimental results, and insights into the impact of different training strategies. They also highlight areas for future research and acknowledge the limitations of their work. Overall, the paper opens up new avenues for theoretical and practical work in the field of delayed next-token prediction.
783 word summary
Researchers from Carnegie Mellon University and Google Research have proposed a new approach to training language models called "pause-training." In this approach, a sequence of pause tokens is appended to the input prefix, and the model's outputs are delayed until the last pause token is seen. This allows the model to process extra computation before committing to an answer. The researchers conducted experiments on decoder-only models with 1B and 130M parameters and found that pause-training showed gains on various downstream tasks when the model was both pre-trained and fine-tuned with delays. Notably, the pause-trained model achieved an 18% improvement in exact match score on the SQuAD question-answering task compared to the standard model. The researchers also explored different combinations of pause-training during pretraining and finetuning and found that introducing delays in both stages yielded the best results. However, introducing delays only during finetuning showed mixed results and even led to a drop in performance in some cases. The researchers conducted key ablations to further investigate the effects of pause-training. They found that appending pause tokens was generally better than prepending them, and there was an optimal number of pause tokens for each downstream task. They also tested the robustness of pause-trained models to varying numbers of inference-time pause tokens and found that the models were relatively robust to test-time distribution shifts. However, providing zero delay during inference significantly decreased performance. The researchers discussed several open questions and future research directions, including understanding the computational advantages of delays, formalizing capacity expansion without parameter expansion, and investigating the interplay between representational capacity and computational pathways in language models. They also compared their work to related approaches, such as chain-of-thought prompting and lightweight finetuning techniques, highlighting the differences and unique contributions of pause-training. Overall, the research demonstrates the benefits of incorporating delays into language model training and inference and opens up new possibilities for improving model performance and understanding the underlying mechanisms.
Training Language Models With Pause Tokens is a research paper that introduces a new approach to training language models. The authors propose the use of "pause" tokens during training to improve the model's performance on various tasks. The key idea is to train the model with dummy pause tokens so that it can learn to make use of additional computation during inference. This approach goes beyond the traditional paradigm of immediate next-token prediction.
The authors demonstrate that training with pause tokens can improve performance on a variety of tasks, but they also acknowledge that not every task may benefit from this approach. Some tasks may be better off without any pause tokens. Additionally, the authors point out that the cost of pause pretraining makes it less accessible for widespread use. They also mention several areas for future research, such as studying the gains across different model sizes and architectures, understanding the underlying mechanism of pause tokens, and exploring different algorithms for pause training.
The paper provides a detailed explanation of the Transformer model, which is used in their experiments. They describe the operations involved in the Transformer block and the generation of the next token. They also provide additional results on downstream finetuning performance for both a 1B model and a 130M model. The results show that pause training outperforms standard training on various tasks, including reasoning tasks, fact recall tasks, and question-answering tasks.
The authors also investigate the effect of prepending or appending pause tokens during training. They find that appending the pause tokens generally yields better performance, although there are some mixed results depending on the task. They further explore the impact of varying the number of pause tokens used during finetuning and find that there is an optimal number of pause tokens for each dataset.
The paper also examines the robustness of pause-trained models to shifts in the number of inference-time pauses compared to the number used during finetuning. It is observed that pause training degrades gracefully with shifts in the number of tokens seen, except for one task where there is a drop in performance when the delay is removed during inference.
The paper concludes by providing a list of downstream datasets used in their experiments and the corresponding hyperparameters for finetuning. The authors also provide the architecture details for the models considered in their work.
In summary, the paper introduces the concept of pause tokens for training language models and demonstrates their effectiveness on various tasks. The authors provide detailed explanations of the Transformer model, experimental results, and insights into the impact of different training strategies. They also highlight areas for future research and acknowledge the limitations of their work. Overall, the paper opens up new avenues for theoretical and practical work in the field of delayed next-token prediction.